Qin, Yujia

Parameter-efficient fine-tuning of large-scale pre-trained language models

Ding, N., Qin, Y., Yang, G., Wei, F., Yang, Z., Su, Y., Hu, S., Chen, Y., Chan, C.-M., Chen, W., et al. (2023) · Nature Machine Intelligence

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 1,191

Tool Learning with Foundation Models

Qin, Y., Hu, S., Lin, Y., Chen, W., Ding, N., Cui, G., Zeng, Z., Huang, Y., Xiao, C., Han, C., et al. (2023) · arXiv

AI-based Problem Solving and PlanningArtificial Intelligence Applications

Cited by 184

Enhancing Chat Language Models by Scaling High-quality Instructional Conversations

Ding, N., Chen, Y., Xu, B., Qin, Y., Hu, S., Liu, Z., Sun, M., & Zhou, B. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Speech and dialogue systemsTopic Modeling

Cited by 130

DebugBench: Evaluating Debugging Capability of Large Language Models

Tian, R., Ye, Y., Qin, Y., Cong, X., Lin, Y., Pan, Y., Wu, Y., Haotian, H., Weichuan, L., Liu, Z., et al. (2024) · Findings of the Association for Computational Linguistics ACL 2024

Software Testing and Debugging TechniquesNatural Language Processing Techniques

Cited by 54

AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors

Chen, W., Su, Y., Zuo, J., Yang, C., Yuan, C., Chan, C.-M., Yu, H., Lu, Y., Hung, Y.-H., Qian, C., et al. (2023) · arXiv

Multi-Agent Systems and NegotiationDistributed Control Multi-Agent Systems

Cited by 39

CREATOR: Tool Creation for Disentangling Abstract and Concrete Reasoning of Large Language Models

Qian, C., Han, C., Fung, Y., Qin, Y., Liu, Z., & Ji, H. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023

Topic ModelingNatural Language Processing Techniques

Cited by 24

StableToolBench: Towards Stable Large-Scale Benchmarking on Tool Learning of Large Language Models

Guo, Z., Cheng, S., Wang, H., Liang, S., Qin, Y., Li, P., Liu, Z., Sun, M., & Liu, Y. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 22

Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Models

Ding, N., Qin, Y., Yang, G., Wei, F., Yang, Z., Su, Y., Hu, S., Chen, Y., Chan, C.-M., Chen, W., et al. (2022) · arXiv

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 20

ML-Bench: Evaluating Large Language Models and Agents for Machine Learning Tasks on Repository-Level Code

Tang, X., Liu, Y., Cai, Z., Shao, Y., Lu, J., Zhang, Y., Deng, Z., Hu, H., An, K., Huang, R., et al. (2023) · arXiv

Software Engineering ResearchTopic Modeling

Cited by 2

DebugBench: Evaluating Debugging Capability of Large Language Models

Tian, R., Ye, Y., Qin, Y., Cong, X., Lin, Y., Pan, Y., Wu, Y., Hui, H., Liu, W., Liu, Z., et al. (2024) · arXiv

Software Engineering ResearchSoftware Testing and Debugging Techniques

Cited by 2